My research question is: To what extent does fine-tuning an AI model improve its effectiveness as a beginner tutor of the instrument tabla? The project will compare the tutoring capabilities of a model that receives additional tabla-related data (fine-tuning) with one that does not, while holding the base model and prompt-engineered questions constant. In other words, this project tests whether a base model already has sufficient understanding of tabla to deliver accurate, comprehensible lessons through prompting alone, or whether additional tabla-specific training improves its instructional quality. As the majority of AI training data comes from Europe and the US, this project demonstrates how targeted fine-tuning can make AI a culturally authentic and inclusive tutor, showing that models trained beyond Western-centric data can meaningfully support regionally or culturally-specific topics like the tabla.

The project will consist primarily of observational and experimental research, as I’ll be systematically testing identical questions and comparing each model’s responses. It will also require some internet research to locate tabla-related datasets and conduct the fine-tuning process. My final product will consist of a comprehensive objective analysis on which model performs better and why, along with individual statistics on what kind of questions each model responded better to and in what way. I will also produce an AI model capable of providing beginners, including myself, with an introductory overview of playing tabla.